Electroencephalography (EEG) is a well-established method for assessing brain activity. When measurement electrodes are attached on the skin of the skull surface, the weak biopotential signals generated in brain cortex may be recorded and analyzed. The EEG has been in wide use for decades in basic research of the neural systems of the brain as well as in the clinical diagnosis of various central nervous system diseases and disorders.
The EEG signal represents the sum of excitatory and inhibitory potentials of large numbers of cortical pyramidal neurons, which are organized in columns. Each EEG electrode senses the average activity of several thousands of cortical pyramidal neurons.
The EEG signal is often divided into four different frequency bands: Delta (0.5-3.5 Hz), Theta (3.5-7.0 Hz), Alpha (7.0-13.0 Hz), and Beta (13.0-32.0 Hz). In an adult, Alpha waves are found during periods of wakefulness, and they may disappear entirely during sleep. Beta waves are recorded during periods of intense activation of the central nervous system. The lower frequency Theta and Delta waves reflect drowsiness and periods of deep sleep.
Different derangements of internal system homeostasis disturb the environment in which the brain operates, and therefore the function of the brain and the resulting EEG are disturbed. The EEG signal is a very sensitive measure of these neuronal derangements, which might be reflected in the EEG signal either as changes in membrane potentials or as changes in synaptic transmission. A change in synaptic transmission occurs whenever there is an imbalance between consumption and supply of energy in the brain. This means that the EEG signal serves as an early warning of a developing injury in the brain.
According to the present state of knowledge, the EEG signal is regarded as an effective tool for monitoring changes in the cerebral state of a patient. Diagnostically, the EEG is not specific, since many systemic disorders of the brain produce similar EEG manifestations. In Intensive Care Units, for example, an EEG signal may be of critical value, as it may differentiate between broad categories of psychogenic, epileptic, metabolic-toxic, encephalopatic and focal conditions.
Suppression is a peculiar EEG waveform which is relatively often encountered with various patient groups. Suppression waveforms may occur in deep anesthesia, coma, severe encephalopathy, hypothermia, hypoxic or ischemic brain injury, structural brain damage, and status epilepticus, for example. In other words, suppression may be caused by anesthetics, even to the neurologically healthy patients, or by (endogenous) neurological dysfunction. Suppression is often encountered in a combination of two alternating patterns: burst and suppression. The waveform of the two alternating patterns is called burst-suppression (BS). An exceptionally clear BS pattern is depicted in FIG. 1 in which EEG periods with burst waveform are indicated with B and those with suppressions with S.
Typically, burst patterns of neurologically healthy patients are sinusoidal-type waveforms, whereas burst patterns of neurologically ill patients may contain spiky waveforms resembling epileptic spikes (epileptiform activity). Although definitions vary, during suppression the EEG amplitude is generally below 10 μV and during bursts of the order of 100 μV.
Due to the rapidly changing dynamics of the EEG, burst suppression introduces many problems, which make accurate detection of burst suppression notoriously difficult. While FIG. 1 shows an example of a burst suppression pattern with clearly separable bursts and suppressions, FIG. 2 depicts an example of more commonly encountered BS waveforms, in which the discrimination of bursts and suppressions is much more difficult. Due to the difficulties, burst suppression is usually detected separately in current brain wave monitors, and a dedicated algorithm is used during burst suppression waveforms to evaluate the cerebral status of the patient during BS waveforms.
An example of an EEG monitoring device resting the detection of burst suppression is disclosed in U.S. Patent Application Publication US 2005/0137494 A1, which describes a method for determining the cerebral state of a patient using generalized spectral entropy of the EEG signal. In this method, portions of the EEG signal data containing artifacts are discarded and the remaining portions are further divided into those in which burst suppression is present and those in which burst suppression is not present. For portions of the EEG signal data in which the signal is stationary in nature and for portions in which only bursts are present, the spectral entropy is determined by their respective algorithms.
Burst suppression EEG monitoring is traditionally and most often performed by calculating a so-called burst suppression ratio (BSR), which represents the temporal proportion of suppressed EEG periods in the EEG signal. BSR calculation is available in most diagnostic EEG devices as well as in devices monitoring the depth of anesthesia. Burst suppression, as a phenomenon, has variable time characteristics. As FIG. 1 depicts, the lengths of the successive bursts and suppressions may vary. Although that information is utilized, for example, as a parameter called inter-burst-interval, which is available in some commercial EEG devices, the application area of the parameter is still unclear. BSR is typically derived over a time window of one minute, which makes it relatively insensitive to the variations in the lengths of successive bursts and suppressions, and thus also a stable indicator of the patient state. Algorithms used for the detection of burst suppression pattern are fine-tuned for the purposes of BSR calculation. Therefore, these algorithms utilize EEG data in relatively long time windows for the detection of suppressed EEG periods. For example, the time window of the algorithm described in [Sarkela M. et al. Automatic analysis and monitoring of burst suppression in anesthesia, Journal of Clinical Monitoring and Computing. 2002; 17:125-134.] is one second. Algorithms employing such long time windows are not able to define exact onsets and offsets of the burst patterns and, as such, are not suitable for the detailed characterization of the burst patterns. Additionally, these algorithns are not optimized to detect short epileptiform spikes occurring during suppression or short suppressions occurring during bursts, because these short duration events do not have a remarkable effect on the one-minute BSR value. As stated above, BSR utilizes only the suppressed EEG periods, which makes it inapplicable for the characterization of the burst patterns occurring between the suppressed EEG periods. Currently there are no automated methods for burst classification or characterization available.
However, there is a clear need for detailed analysis of bursts during BS waveforms, as bursts contain valuable information on the abnormalities of the brain, cf. [Young G B, McLachlan R S, Kreeft J H, Demelo J D: An Electoencephalographic Classification for Coma, Can. J. Neurol. Sci. 1997; 24:320-3259]. For the correct diagnosis and proper treatment it is therefore important to recognize epileptiform burst patterns from other burst parters, especially in the case of status epilepticus patients. Nevertheless, current monitors only use the relative amount of suppressions (BSR) in their analysis during BS.
The present invention seeks to alleviate the problems caused by suppression waveforms in the analysis of physiological signal data, especially EEG, by bringing about a novel mechanism for alleviating the effects of suppression waveforms in patient monitoring.